Satellite and Street Imagery-Informed Calibration of Physics-Based Rainfall-Induced Slope Susceptibility Maps
Publication: International Conference on Transportation and Development 2024
ABSTRACT
Physics-based predictive models for identifying rainfall-induced slope susceptibilities typically consider slope geometry, soil type and properties, and precipitation. However, these models do not often account for the impact of land cover (e.g., retaining structures, vegetation, wetlands, and water bodies) on slope susceptibilities. The presence of retaining structures and vegetation can improve slope stability, while exposure of slope to water bodies can adversely degrade slope stability. Existing data sources, such as the US Department of Agriculture (USDA) and Texas Natural Resources Information System (TNRIS) databases, lack sufficient land cover information, making it challenging to derive the necessary data. The objectives of this research are to develop a satellite and street imagery-informed calibration method and integrate it with physics-based models to incorporate the effects of land cover on slope stability. The methodology comprises creating a formal workflow for collecting images along highway corridors through high-resolution satellite and street images, classifying the condition of land cover through assessment of these images, and developing the polygon feature layer representing the extent of different land covers. This method is integrated with an existing physics-based model by transforming the polygon feature layer into a raster file and conducting a raster overlay analysis of the slope susceptibility maps to update the susceptibility levels of slopes. This integration method was implemented along the highway corridors in Texas. Over 10,000 slope assessments were calibrated utilizing satellite and street imagery to identify instances of water-eroded and mechanically stabilized slopes. Consequently, the susceptibility levels in these locations were adjusted to account for the impact of the observed land cover conditions. This integration method enhances the accuracy of slope failure susceptibility maps, enabling transportation agencies to identify critical slope segments and plan proactive slope maintenance initiatives more effectively.
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International Conference on Transportation and Development 2024
Pages: 647 - 656
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Published online: Jun 13, 2024
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